Research Engineer, Synthetic Data

About the Role This is a Research Engineer role focused on synthetic data, sitting within a roughly 15-person engineering team of Olympiad medalists and published researchers. You will build the pipelines that turn domain-specific workflows into scalable, high-quality training tasks for AI agents, directly shaping what models learn and how well they perform. What You'll Do

• Build end-to-end synthetic data pipelines that transform domain-specific workflows into realistic, structured, and challenging training tasks.
• Collaborate with subject-matter experts to create synthetic tasks for AI agents across professional and technical domains.
• Design task generation methods that produce diverse, realistic, and learnable outputs at scale.
• Build tooling to mutate, validate, and continuously improve synthetic tasks.
• Analyze model and agent performance on synthetic tasks to identify what they teach and where they break down.
• Develop metrics to quantify synthetic task diversity, realism, learnability, and overall quality. What We're Looking For
• 2 to 4 years of experience in software engineering, machine learning engineering, or AI research, with hands-on work building data pipelines, ML infrastructure, or synthetic data systems.
• Proficiency in Python and experience developing in Linux environments using containerization tools such as Docker.
• Demonstrated experience applying synthetic data research methods to build end-to-end data generation pipelines for AI/ML applications.
• Strong understanding of synthetic data quality criteria and evaluation metrics, including diversity, realism, and learnability, as well as their inherent limitations.
• Experience designing, implementing, or maintaining evaluation frameworks, benchmarks, or testing environments for AI agents or large language models.
• Track record of independently owning and delivering technical projects end-to-end with minimal predefined requirements.
• Experience building automated systems to generate, validate, mutate, or process structured datasets at scale.
• Sharp eye for edge cases, inconsistencies, and quality issues in synthetic or algorithmically generated data.
• Familiarity with reinforcement learning training paradigms, agentic AI workflows, or LLM post-training pipelines is a plus.
• Comfortable operating in unstructured, early-stage environments and reasoning from first principles.
• Strong communication skills for asynchronous, cross-timezone collaboration. Compensation & Benefits Salary range: $150,000 to $250,000 USD annually. Visa sponsorship is available. Location On-site in Singapore .